# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import os from collections.abc import Callable from concurrent.futures import Future, ThreadPoolExecutor from functools import cached_property from multiprocessing import Lock from typing import Any import torch import torch.distributed as dist import vllm.envs as envs from vllm.logger import init_logger from vllm.utils.network_utils import get_distributed_init_method, get_ip, get_open_port from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput from vllm.v1.engine import ReconfigureDistributedRequest, ReconfigureRankType from vllm.v1.executor.abstract import Executor from vllm.v1.outputs import AsyncModelRunnerOutput, DraftTokenIds, ModelRunnerOutput from vllm.v1.serial_utils import run_method from vllm.v1.worker.worker_base import WorkerWrapperBase logger = init_logger(__name__) class UniProcExecutor(Executor): def _init_executor(self) -> None: """Initialize the worker and load the model.""" self.driver_worker = WorkerWrapperBase(rpc_rank=0) distributed_init_method, rank, local_rank = self._distributed_args() kwargs = dict( vllm_config=self.vllm_config, local_rank=local_rank, rank=rank, distributed_init_method=distributed_init_method, is_driver_worker=True, shared_worker_lock=Lock(), ) self.async_output_thread: ThreadPoolExecutor | None = None if self.max_concurrent_batches > 1: self.async_output_thread = ThreadPoolExecutor( max_workers=1, thread_name_prefix="WorkerAsyncOutput" ) self.driver_worker.init_worker(all_kwargs=[kwargs]) self.driver_worker.init_device() self.driver_worker.load_model() def _distributed_args(self) -> tuple[str, int, int]: """Return (distributed_init_method, rank, local_rank).""" distributed_init_method = get_distributed_init_method(get_ip(), get_open_port()) # set local rank as the device index if specified device_info = self.vllm_config.device_config.device.__str__().split(":") local_rank = int(device_info[1]) if len(device_info) > 1 else 0 return distributed_init_method, 0, local_rank @cached_property def max_concurrent_batches(self) -> int: return 2 if self.scheduler_config.async_scheduling else 1 def collective_rpc( # type: ignore[override] self, method: str | Callable, timeout: float | None = None, args: tuple = (), kwargs: dict | None = None, non_block: bool = False, single_value: bool = False, ) -> Any: if kwargs is None: kwargs = {} if not non_block: result = run_method(self.driver_worker, method, args, kwargs) return result if single_value else [result] try: result = run_method(self.driver_worker, method, args, kwargs) if isinstance(result, AsyncModelRunnerOutput): if (async_thread := self.async_output_thread) is not None: if single_value: return async_thread.submit(result.get_output) def get_output_list() -> list[Any]: return [result.get_output()] return async_thread.submit(get_output_list) result = result.get_output() future = Future[Any]() future.set_result(result if single_value else [result]) except Exception as e: future = Future[Any]() future.set_exception(e) return future def execute_model( # type: ignore[override] self, scheduler_output: SchedulerOutput, non_block: bool = False ) -> ModelRunnerOutput | None | Future[ModelRunnerOutput | None]: return self.collective_rpc( "execute_model", args=(scheduler_output,), non_block=non_block, single_value=True, ) def sample_tokens( # type: ignore[override] self, grammar_output: GrammarOutput | None, non_block: bool = False ) -> ModelRunnerOutput | None | Future[ModelRunnerOutput | None]: return self.collective_rpc( "sample_tokens", args=(grammar_output,), non_block=non_block, single_value=True, ) def take_draft_token_ids(self) -> DraftTokenIds | None: return self.collective_rpc("take_draft_token_ids", single_value=True) def check_health(self) -> None: # UniProcExecutor will always be healthy as long as # it's running. return def reinitialize_distributed( self, reconfig_request: ReconfigureDistributedRequest ) -> None: self.driver_worker.reinitialize_distributed(reconfig_request) if ( reconfig_request.new_data_parallel_rank == ReconfigureRankType.SHUTDOWN_CURRENT_RANK ): self.shutdown() def shutdown(self) -> None: if worker := self.driver_worker: worker.shutdown() class ExecutorWithExternalLauncher(UniProcExecutor): """An executor that uses external launchers to launch engines, specially designed for torchrun-compatible launchers, for offline inference with tensor parallelism. see https://github.com/vllm-project/vllm/issues/11400 for the motivation, and examples/offline_inference/torchrun_example.py for the usage example. The key idea: although it is tensor-parallel inference, we only create one worker per executor, users will launch multiple engines with torchrun-compatible launchers, and all these engines work together to process the same prompts. When scheduling is deterministic, all the engines will generate the same outputs, and they don't need to synchronize the states with each other. """ def _init_executor(self) -> None: """Initialize the worker and load the model.""" assert not envs.VLLM_ENABLE_V1_MULTIPROCESSING, ( "To get deterministic execution, " "please set VLLM_ENABLE_V1_MULTIPROCESSING=0" ) super()._init_executor() def _distributed_args(self) -> tuple[str, int, int]: # engines are launched in torchrun-compatible launchers # so we can use the env:// method. # required env vars: # - RANK # - LOCAL_RANK # - MASTER_ADDR # - MASTER_PORT distributed_init_method = "env://" rank = int(os.environ["RANK"]) local_rank = int(os.environ["LOCAL_RANK"]) return distributed_init_method, rank, local_rank def determine_available_memory(self) -> list[int]: # in bytes # we need to get the min across all ranks. memory = super().determine_available_memory() from vllm.distributed.parallel_state import get_world_group cpu_group = get_world_group().cpu_group memory_tensor = torch.tensor([memory], device="cpu", dtype=torch.int64) dist.all_reduce(memory_tensor, group=cpu_group, op=dist.ReduceOp.MIN) return [memory_tensor.item()]